Abstract
Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planning of lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.
Highlights
There are abundant unexploited resources on the moon
The lunar robot is an indispensable part of the lunar exploration project, and the path planning is the guarantee for the lunar robot to complete its tasks safely and accurately in the lunar environment
Aiming at the problems of falling into local optimum, slow convergence, and dynamic obstacle avoidance in ant colony algorithm, firstly, we propose an improved potential field ant colony algorithm based on dynamic obstacle avoidance
Summary
There are abundant unexploited resources on the moon. Many countries are promoting the lunar exploration project actively. In various traffic planning problems, route planning has been widely studied and discussed These algorithms mainly include A* algorithm, simulated annealing algorithm, genetic algorithm, differential evolution algorithm, particle swarm optimization algorithm, ant colony algorithm, and so on. The basic idea is to find the shortest path according to the concentration of pheromone left by ants In these methods, with the increasing complexity of obstacles, the convergence speed of genetic algorithm, simulated annealing algorithm, A* algorithm, and differential evolution algorithm will slow down. The adaptive potential field ant colony algorithm is used to do the path planning This method adjusts the heuristic function dynamically by introducing the inducing heuristic factor, so as to improve the global optimization ability and accelerate the convergence speed.
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